Method and system to characterize transcriptionally active regions and quantify sequence abundance for large scale sequencing data

ABSTRACT

This invention provides a quantitative method to determine transcriptionally active regions and quantify sequence abundance from large scale sequencing data. The invention also provides a system based on reference sequences to design and implement the method. The system processes large scale sequence data from high throughput sequencing, generates transcriptionally active region sequences as necessary, and quantifies the sequence abundance of the gene or transcriptionally active region. The method and system are useful for many analyses based on RNA expression profiling.

FIELD OF THE INVENTION

This invention relates generally to a sequence data analysis system using methods for processing nucleic acid sequencing output-with a reference sequence based design to identify transcriptionally active regions and quantify the sequence abundance which can be used for expression profiling analysis.

DESCRIPTION OF THE RELATED ART

DNA sequencing technology has played a key role in the advancement of genomic study and research for health sciences for more than 10 years. At the same time, the completion of the human genome sequencing project has been recognized as one of the most significant milestones for research and drug development. Recently several next generation sequencing technologies have made it possible to read billions of nucleotides in a single experiment. The availability of billion nucleotide scale sequencing and its expected cost reduction has expanded the potential applications of sequencing to area such as RNA expression profiling and genome scale biomarker discovery that historically have not been possible.

RNA expression profiling is used by researchers to measure the quantities of many different RNAs transcribed from the genome in various cell types and cell growth stages. It is also used as a diagnostic method to help predict a patient's risk for disease and response to treatment. Common forms of RNA expression profiling include gene expression profiling, exon expression profiling, non-coding RNA expression profiling, and whole transcriptome profiling. Methods of RNA expression profiling are often performed in high throughput platforms and typically comprises the following steps:

-   -   1. Isolate RNAs from a biological sample.     -   2. Convert the RNAs isolated into to their complementary DNAs         (cDNAs) typically through reverse transcription.     -   3. Quantify the level of cDNAs using high throughput platform         such as microarray and large scale quantitative PCR (QPCR).         Recently large scale sequencing been utilized for cDNA         quantification as well.     -   4. Employ a quantitative analysis method to analyze the RNA         level association with biological processes (e.g. disease).

The cost of DNA sequencing technology has been trending down quickly and the trend will continue for decades to come. Sequencing technology is becoming the preferable choice for expression profiling due to its higher throughput, higher accuracy, and more flexibility for small and non-coding RNA expression profiling. However, application of high throughput sequencing for RNA expression profiling and genome scale biomarker discovery may encounter issues in the following areas:

Transcriptionally active region identification: In order to profile RNA expression, RNA sequences or their equivalent DNA sequences to be used for the expression profiling analysis need to be determined. In this application, these RNA sequences or their equivalent DNA sequences for the expression profiling analysis are defined as transcriptionally active regions. Typical transcriptionally active regions may be represented by RNA sequences, cDNA sequences, exon sequences, and non-coding RNA or equivalent DNA sequences. Within this context, the transcriptionally active regions can be conceptualized as the transcribed regions of the human genome. The genes used for RNA expression profiling have been mostly protein coding sequences in the past. In recent years, accumulating evidence has shown non-coding DNA plays a vital role in the regulation of gene expression during normal and disease processes. To find non-coding sequences as biomarkers and profile their expressions become desirable and critical for many genomics studies. However, for the less known non-coding RNAs including microRNA and many other small RNAs, their genomic transcribed regions are often not well defined. The sequencing output sequences for these non-coding regions are often not well-aligned due to either biological or technical reasons. This inexact mapping makes it very difficult to derive the transcriptionally active regions for expression profiling analysis.

Sequence abundance quantification: When using sequencing for RNA expression profiling, there is no well-defined method to quantify the gene or transcriptionally active region sequence abundance. In high throughput setting, quantifying the number of nucleotide sequences is a challenge because the output sequences usually do not align exactly to the reference sequences even though they are derived from the same gene. This often results from many well-known biological processes such as alternative splicing, alternative transcription initiation, alternative transcription termination, RNA degradation, etc. Additionally, the reverse transcription to convert RNAs into complementary DNAs (cDNAs) for sequencing is often partial or incomplete. As a result, the output sequences generated by sequencing even for the same gene are often heterogeneous, have various length and/or various sequence composition as the case illustrated in FIG. 2. This inexact mapping of sequencing output sequences makes the quantification of the RNA levels for the genes or transcriptionally active regions difficult and not straightforward.

Computation intensity: Many sequence analyses such as genome sequence assembly or EST sequence clustering can be fundamentally transformed into iteration of the alignment of two sequences, i.e. pairwise sequence alignment. For large sequence data analysis this pairwise sequence alignment usually is the limiting factor. The computing intensity and cost is high due to the complexity of sequence alignment algorithms such as Needleman-Wunsch algorithm (Needleman and Wunsch 1970), Smith-Waterman algorithm (Smith and Waterman 1981), or other similar algorithms. An efficient method to reduce computing intensity for pairwise sequence alignment is highly desirable for large scale sequence analyses.

SUMMARY OF THE INVENTION

Here we provide a sequence data analysis system using a reference sequence based design to implement the method for transcriptionally active region identification and sequence abundance quantification. The sequence data analysis system uses a method developed to process sequencing output sequences to find transcriptionally active regions as necessary and quantify the transcriptionally active region sequence abundance, which can be used for expression profiling analysis.

Another object of the present invention is to provide a quantitative method to define a non-coding transcriptionally active region given the inexact aligned sequences from high throughput sequencing. The method can be implemented in a computing system that discovers the transcriptionally active regions with adjustable parameters from the high throughput sequencing data.

Yet another object of the present invention is to provide a method to quantify the inexact mapped output sequences for a given gene or transcriptionally active region sequence and generate a quantitative abundance score that can be used for expression profiling analysis.

In one embodiment of the present invention, a method is provided for discovery of transcriptionally active regions. In this method, sequences outputted by sequencing equipment and a reference sequence are provided and the output sequences are aligned to the reference sequences. The sequence alignments are parsed and transformed into reference sequence based coordinates. A coverage count is then calculated for each nucleotide position in the reference sequence and a contiguous reference sequence region is located where the coverage count is equal to or greater than a threshold.

In another embodiment of the present invention, a system is disclosed for discovery of transcriptionally active regions. The system comprises a first processor for aligning sequences outputted by sequencing equipment and a reference sequence, a database for collecting reference sequence based coordinates, a second processor for calculating a coverage count for each nucleotide position in said reference sequence and locating a contiguous reference sequence region where the coverage count is equal to or greater than a threshold, and a computer output device for displaying transcriptionally active region profiles.

In another embodiment of the present invention, a method is provided for RNA expression profiling. In this method, sequences outputted by sequencing equipment and a reference sequence are provided and the output sequences are aligned to the reference sequences. The sequence alignments are parsed and transformed into reference sequence based coordinates. A coverage count is then calculated for each nucleotide position in the reference sequence and a contiguous reference sequence region is identified where the coverage count is equal to or greater than a threshold. Once the coverage count for each nucleotide in the transcriptionally active regions is determined an abundance score is calculated for each transcriptionally active region.

In another embodiment of the present invention, a system is disclosed for RNA expression profiling. The system comprises a first processor for aligning sequences outputted by sequencing equipment and a reference sequence, a database for collecting reference sequence based coordinates, a second processor for calculating a coverage count for each nucleotide position in said reference sequence and locating a contiguous reference sequence region where the coverage count is equal to or greater than a threshold, a third processor for calculation of abundance scores; and a computer output device for displaying RNA expression profiles.

In another embodiment of the present invention, a method is provided for identifying diagnostic and prognostic markers of disease. In this method, sequences outputted by sequencing equipment from normal and disease samples and a reference sequence are provided and the output sequences are aligned to the reference sequences. The sequence alignments are parsed and transformed into reference sequence based coordinates. A coverage count is then calculated for each nucleotide position in the reference sequence and a contiguous reference sequence region is identified where the coverage count is equal to or greater than a threshold. Once the coverage count for each nucleotide in the transcriptionally active regions is determined, an abundance score is calculated for each transcriptionally active region in both the normal and disease samples. From this data, differential expression patterns of transcriptionally active regions are identified between normal and disease samples.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow chart of the sequence data analysis processes.

FIG. 2 illustrates the alignment of output sequences against a reference sequence SEQ ID NOS 1-9, respectively in order of appearance).

FIG. 3 illustrates how a high coverage sequence region is identified and its abundance score calculated using the sequence alignments in FIG. 2 (SEQ ID NO: 9).

FIG. 4 illustrates the mapping of an intronic sense EST (ISE) transcriptionally active regions (also known as “hotspots”).

FIG. 5 illustrates the Abundance Score, also known as “hotness”, calculation (SEQ ID NOS 10-13, respectively in order of appearance).

FIG. 6 illustrates an example of ISE transcriptionally active regions with an integer abundance score.

FIG. 7 illustrates abundance score distributions for the ISE transcriptionally active regions (a) and exons in RefSeq (b).

DETAILED DESCRIPTION OF THE INVENTION

RNA levels from biological samples are routinely quantified on high throughput platforms such as microarray and QPCR instruments for expression analysis. As the cost of sequencing technology goes down, the application of sequencing to RNA quantification and expression profiling becomes attractive. Two technical hurdles currently make the analysis difficult when applying sequencing for expression profiling. 1) Quantification of the RNA level according to the sequencing output reads is not well defined, and 2) Non-coding gene sequences are not well defined.

The instant patent application, discloses a method to quantify RNA levels, and a method to define the transcriptionally active regions at single nucleotide resolution. The transcriptionally active regions thus defined can be used as surrogates for non-coding genes for expression profiling analysis.

Expression profiling is intended to accurately determine the RNA abundance for a given gene. In microarray, the abundance is measured by hybridization signal. In QPCR, the abundance is measured by the nucleotide amplification fractional cycle number (Ct or Cp) to reach a threshold. In sequencing, the abundance is measured by sequence count. From the technology perspective, the abundance measurement accuracy in theory should be in increasing order:

-   -   Microarray<<QPCR<<Sequencing         However, no known method has provided a good solution for         sequence abundance counting, especially for non-coding gene         targets. The sequence data analysis system of the instant         application provides a solution to this problem.

Because most of the non-coding gene regions have been loosely defined; non-coding genes pose another challenge for expression profiling. In the sequence data analysis system of the instant application, a quantitative solution is provided to clearly define a transcriptionally active region which can be used as a surrogate for non-coding genes, especially where the start and end positions for most non-coding genes are not well-characterized.

Sequence Data Analysis System Design based on Reference Sequence Coordinates

Sequence analysis is the core task for the sequence data analysis system disclosed herein. In order to reduce the computing cost associated with pairwise sequence alignment, we designed a reference sequence based coordinates that effectively transform the pairwise sequence alignments into simpler and faster coordinate comparison. The principle behind the design is to anchor large set of sequences onto reference sequences, represent each sequence with its coordinates based on the positions of its alignment to the reference sequences, and perform the coordinate comparisons. One simple implementation is to use a pair of the reference sequence nucleotide position indices as the coordinates to represent a sequence start and end positions. With this implementation, a pairwise sequence alignment task can often be converted into a task to determine whether the two sequence fragments anchored onto reference sequences overlap or not. For example, given the two sequences, e.g. SequenceA and SequenceB, with the two following reference coordinates:

-   -   SequenceA: StartA, EndA     -   SequenceB: StartB, EndB         the following Boolean logic or its equivalent can be used for         the overlap query:         Overlap Query Form 1:

( StrandA > StrandB AND NOT (StrandA >= EndB AND EndA >= EndB) ) OR ( StrandA < StrandB AND NOT (StrandB >= EndA AND EndB >= EndA) ) OR ( StrandA = StrandB )

One particularly useful constraint on the reference coordinates is to implement them in a way that the start position is always less than or equal to the end position, or equivalently that the start position is always greater than or equal to the end position. Throughout this application, the former implementation is used and implied as an example to illustrate. However the latter implementation should work equally well. With this constraint, the start and end positions in the coordinates are relative to the anchoring reference sequences indices and do not correlate to biological start or end information such as transcription start or end. With this start-end position constraint, the overlap query can be much simplified into the following Boolean logic:

Overlap Query Form 2:

( EndA >= StartB ) AND ( StartA <= EndB ) which effectively reduce the computation complexity associated with sequence overlap query when implemented.

The overlap Boolean logic above assumes minimum overlap length is one sequence base, i.e. two sequences share at least one bases. To achieve more flexibility control on minimum overlap length (minOverlapLength), an adjustment on the coordinates can be used and the overlap Boolean logic can take the following form with this start-end position constraint:

Overlap Query Form 3:

( EndA − (minOverlapLength − 1) >= StartB + (minOverlapLength − 1) ) AND ( StartA + (minOverlapLength − 1) <= EndB − (minOverlapLength − 1) )

In the instant sequence data analysis system, the overlap query form 2 was implemented as the default. However, the overlap query based on the coordinate comparison in any forms mentioned above can be executed much faster than most known sequence alignment method. The following computing time complexity estimations illustrate the benefit of reference sequence coordinates.

To compare each sequence from one dataset to each sequence from a second dataset given two sequence datasets with size of N and M, the computing time complexity for pairwise sequence alignments is approximated by O(N*M). When N is about the same size of M, this is essentially a O(N²) algorithm. Whereas the computing time complexity for coordinate comparison method is dominantly demanded by sequence alignment to the reference sequence and approximated by O(N+M). It is essentially a O(N) algorithm when N is about the same size of M. Therefore when sequence data are large, it is much preferable to substitute pairwise sequence alignments with coordinate comparisons.

Besides the computing time complexity benefit for sequence analysis, reference sequence coordinates provide other benefits include

-   -   1. Data storage benefit. Raw sequence data require much larger         storage space than the coordinates representing the sequences.     -   2. Analysis benefit for new sequence dataset. When a new         sequence dataset is to be analyzed with the existing dataset,         the computing demand is limited to the alignment of new         sequences to reference sequences and the output of coordinates         for analysis.     -   3. Computing benefit for high sequence coverage region discovery         and sequence abundance score calculation as described below.     -   4. Locality of information benefit. When implemented in a         computing system, the reference coordinates can often be         indexed. Using indices allows rapid retrieval and computation of         nearby sequences in memory or disk.

With this design principle, a sequence data analysis system was developed to implement the instant method for RNA expression profiling (FIG. 1). The system supports reference sequences and sequencing output sequences as inputs. These two inputs are described as follows.

Reference Sequences

The sequence data analysis system take two inputs. One input is the reference sequences as described above. The reference sequences for sequence data analysis system could be any set of sequences that that are deemed appropriate for anchoring the sequencing output sequences. For example, in a biological setting, reference sequences could be a set of genomic sequences, RNA or cDNA sequences, or even artificial sequences. Reference sequences are pre-determined inputs to the RNA expression profiling system. In one embodiment, the reference sequences implemented in the system are the whole human genome sequences. However, in cases where there are no good existing references, they can be the assembled consensus sequences derived from sequencing output sequences through sequence assembly tools such as Phrap.

Sequencing Output Sequences

Sequencing output sequences in this document refer to the sequences outputted by sequencing equipment typically in a high throughput setting for RNA expression profiling purpose. However they could be any sequence dataset for abundance profiling. Sequencing output sequences are another input to the sequence data analysis system.

The system transforms sequencing output sequences into reference based coordinates, determines the transcriptionally active region sequences as necessary, and quantifies and outputs the abundance scores for the transcriptionally active region sequences. Each process is described as follows.

Alignments of Sequencing Output Sequences to Reference Sequences

Given the inputs of sequencing output sequences and the reference sequences, the sequence data analysis system first aligns the output sequences to the reference sequences by sequence alignment tools. In one embodiment, a linux GMAP program (Wu T, and Watanabe C 2005) was utilized to align all the output sequences to human genome reference sequences. Other sequence alignment tools for alignments of output sequences to reference sequences include BLAT, BLAST, FASTA, and clustalw. An illustration of the alignments of output sequences to the reference sequences is shown in FIG. 2.

Parsing and Transformation of Sequence Alignments into Reference Sequence Based Coordinates

As described above, the reference coordinates for the sequencing output sequences are based on the nucleotide position indices of the reference sequences. In our implementation, the reference sequence nucleotide position indexing is 1-based, i.e., the first nucleotide position in the reference sequences is 1. Other indexing including 0-based indexing would work as well. For the instant sequence data analysis system, whole human genome sequences were used as the reference sequences. The human genome sequences for sequence data analysis system are naturally partitioned into 22 autosomal chromosome sequences (with naming convention of chr1, chr2, . . . , and chr22) plus chromosome X (chrX), chromosome Y (chrY), and a technical chromosome unknown (chr_unknown) to capture certain human genome sequences that can not mapped to any chromosome yet. Since human chromosomes are double stranded, the strandedness information is also captured for reference sequences and the sequencing output sequences coordinates. Therefore from the alignment outputs four components are parsed out; namely chromosome, strand, start position, and end position for each sequencing output sequence according to its alignment onto the reference sequences, and then formatted into the reference coordinates. Given the two output sequences, e.g. SequenceA and SequenceB, with the reference coordinates after parsing and formatting will be:

SequenceA: ChromosomeA, StrandA, StartA, EndA

SequenceB: ChromosomeB, StrandB, StartB, EndB

As described above, these coordinates are 1-based on reference sequences, and a start-end constraint (Start is always less than or equal to End) has been implemented. With these coordinates, the default overlap query form 2 (described above) for sequence analysis will take the following format with added constraints in Boolean logic:

-   -   Overlap Query Form 4:

( ChromosomeA = ChromosomeB ) AND ( StrandA = StrandB ) AND ( EndA >= StartB ) AND ( StartA <= EndB )

-   -   The above form is the logic for our overlap query implementation         for most genome scale sequence analyses.         Determination of Transcriptionally Active Regions

Genes in RNA expression profiling are transcriptionally active regions on the reference sequences, e.g. human genome. Sometimes a set of target genes are predetermined for an RNA expression profiling study. When a set of target genes are provided, the sequence data analysis system will skip the transcriptionally active region determination step and use the pre-determined gene sequences for the following steps. Alternatively, for expression profiling involving large scale transcriptome analysis, e.g. whole transcriptome, a comprehensive set of target genes (especially for the non-coding sequences) is not pre-determined and needs to be identified as a post-sequencing process. In the cases where the target genes are not defined before the sequencing study, the transcriptionally active regions will be defined by the sequence data analysis system and used as the gene surrogates for expression profiling analysis. The way sequence data analysis system finds transcriptionally active regions is based on the sequencing output sequences. However due to the inexact mapping of sequencing output sequences to the reference sequences, it is difficult to find transcriptionally active region sequences with confidence especially for non-coding gene regions. In order to find transcriptionally active regions with higher confidence, in the sequence data analysis system a search for high coverage sequence regions (i.e., the regions that have many sequencing output sequences aligned) is conducted on the reference sequences. The underlying reasoning is that the higher the coverage by sequencing output sequences, the more likely the reference sequence regions transcribe biologically. Therefore, the instant sequence analysis application discloses a quantitative method that is implemented in a computing system for high coverage sequence region discovery in a high throughput setting as described here.

Once the sequencing output sequences have been aligned to the reference sequences, for each nucleotide position in reference sequence a coverage count is calculated and a count histogram can be generated (FIG. 3). Then a high coverage region is determined quantitatively by locating the contiguous reference sequence region where the coverage count is equal to or greater than a threshold (FIG. 3). The high coverage regions discovered thus are the transcriptionally active regions for the following RNA expression profiling analysis.

In the sequence data analysis system, a coverage count threshold of 3 was implemented in the sequence data analysis system. However, the coverage count threshold could be any fractional or integer number greater than or equal to zero

In the sequence data analysis system, a minimal length constraint was also implemented for discovery of the high coverage regions. In the instant implementation, the minimal length cutoff is 20 nucleotides. However it could be any fractional or integer numbers greater than or equal to zero.

In one embodiment, by implementing the high coverage sequence regions discovery algorithm in a C++ program, all the EST transcriptionally active regions, or “hotspots”, were found within all known gene intron regions for the high throughput sequence data (Li, et.al., 2007)

The high coverage sequence regions thus discovered are the transcriptionally active regions as surrogates for genes for the abundance score calculation and RNA expression profiling analysis.

Calculation of Abundance Scores

Once all sequencing output sequences are aligned onto the reference sequence, the coverage count for each nucleotide in the target genes or transcriptionally active regions can be calculated. An abundance score (AS) is calculated for each gene or transcriptionally active region with the following formula (FIG. 3)

${A\; S} = \frac{\begin{matrix} {{Sum}{\mspace{11mu}\;}{of}\mspace{14mu}{coverage}\mspace{14mu}{count}\mspace{14mu}{for}{\mspace{11mu}\;}{each}} \\ {{nucleotide}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{transcription}\mspace{14mu}{active}\mspace{14mu}{region}} \end{matrix}}{{Length}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{transcription}\mspace{14mu}{active}\mspace{14mu}{region}}$

In one embodiment, the transcriptionally active region discovery algorithm was implemented and the abundance scores were calculated for the transcriptionally active sequence regions found through a C++ program for all the EST transcriptionally active regions within all known gene intron regions for the high throughput sequence data (Li, et al., 2007)

Output for Transcriptionally Active Region Profiles

After the calculation of abundance scores, the system will output them along with the genes pre-determined or transcriptionally active regions discovered as the expression profiles. They can then be used for further RNA expression profiling analyses or transcriptionally active region annotation.

For the outputs of the sequence data analysis system, an SQL relational database management system was utilized to store the RNA expression profiles. Implementation using a database solution effectively takes full advantage of database server enterprise hardware to meet the high throughput bioinformatics computing demands that have historically been solved by C++ or JAVA numeric programming. With the SQL server's built-in algorithms, use of constrained data types for genomic coordinates (integer types for start and end nucleotide positions), and indexing capabilities, the overlap queries for sequences analysis can operate efficiently on indexed integers. This SQL database based approach has several advantages:

-   -   1. SQL databases built-in indexing features significantly         improve the efficiency of genomic comparisons which historically         demanded complex programming and data partitioning, i.e.,         binning, essentially trading storage space for computing time.     -   2. SQL databases integer constraints on the data type of start         and end positions exploit the computer's built-in optimization         on native data type computing.     -   3. Clustered indices on SQL database tables optimize data         retrieval operations, exploit locality of information         properties, and reduce the number of data reads, thus         significantly improve the speed of querying large datasets.     -   4. SQL servers have optimal implementations of set operations         such as hashing and bitmaps for matching and sorting data.     -   5. The use of general purpose SQL database servers (e.g.         Microsoft SQL Server 2005) exploits available built-in features         such as multi-threading, caching, 64-bit architecture design,         and memory allocation, etc. with no additional coding needed.

The RNA profiles including the genes pre-determined or transcriptionally active regions discovered and abundance scores generated from the system using the instantly claimed methods are quantitative and biologically significant. There are many known and potential utilities based on expression profiling analysis for the RNA profiles generated. The utilities include but are not limited to discovery of biomarker gene panel for diseases, discovery of new non-coding biomarker gene for diseases, and discovery of new biological functions for known genes.

In summary, the method and system disclosed herein can efficiently process sequencing output sequences for discovery of transcriptionally active regions and quantify the sequence abundances. The transcriptionally active region profiles generated by the system have many utilities based on RNA expression profiling.

EXAMPLE 1 Computational Identification of Human Intronic Sense EST (ISE) Hotspots Reveals Potential Functional RNA Elements

Recent evidence suggests that a large portion of the mammalian genome is transcribed. Expressed sequence tags (ESTs) identified through large sequencing projects mark the transcribed regions of the genome. EST hotspots, genomic regions where many ESTs are mapped, may have significant biological functions and biomarker potential although many of them map to the non-coding portions of genes. The instantly claimed method applies a transcriptionally active region discovery algorithm to identify EST hotspots in the genome and to quantify their “hotness” by calculating an abundance score. Using the public dbEST data, the instant method was able to identify a set of human intronic sense EST (ISE) transcriptionally active regions, or “hotspots”. The results show that the ISE transcriptionally active regions likely represent both coding and non-coding functional RNA elements.

An intronic sense EST (ISE) transcriptionally active region is defined as a contiguous genomic DNA fragment that maps to an intronic region of a known RefSeq annotated transcript and covered by at least three sense ESTs for each nucleotide position (FIG. 4).

The process for providing an abundance score calculation for a transcriptionally active region comprises identifying all RefSeq transcript variants for each Entrez Gene ID and collapsing exons of RefSeq transcript variants into one virtual transcript for each Entrez Gene ID. Consensus introns are then derived from the virtual transcript and all ESTs in the human dbEST that overlap the consensus intron are identified. The EST abundance coverage for each nucleotide in the consensus intron are calculated and contiguous regions with length>=20 nts and EST coverage>=3 are identified as ISE transcriptionally active regions. Finally, an abundance score (AS), or “hotness”, is calculated for each transcriptionally active region as follows:

${A\; S} = \frac{\begin{matrix} {{Sum}{\mspace{11mu}\;}{of}\mspace{14mu}{coverage}\mspace{14mu}{count}\mspace{14mu}{for}{\mspace{11mu}\;}{each}} \\ {{nucleotide}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{transcription}\mspace{14mu}{active}\mspace{14mu}{region}} \end{matrix}}{{Length}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{transcription}\mspace{14mu}{active}\mspace{14mu}{region}}$

FIGS. 3 and 5 illustrate how a transcriptionally active region is identified and its AbundanceScore calculated using the sequence alignments described above.

As shown in Table 1, most ISE transcriptionally active regions overlap predicted regions from two UCSC Genome Browser tracks (Karolchik et al., 2003): 1) ECGene track, predicts spliced gene exons enriched in coding sequences, and 2) 7× Reg Potential track, predicts mostly regulatory non-coding sequences. Thus ISE transcriptionally active regions may represent potential functional RNA elements (defined as genomic sequences coding for functional RNA transcripts). These functional RNA elements may be coding or non-coding regulatory sequences.

TABLE 1 Human ISE transcriptionally active regions Number of human ISE transcriptionally active regions identified: 81,342 Number of Entrez genes having ISE transcriptionally active regions: 12,406 Median length of human ISE transcriptionally active regions: 124 Median Hotness of human ISE transcriptionally active regions: 3.28 Number of ESTs in human dbESTs screened: ~7.3 million Reference human genome assembly: NCBI 35 (UCSC HG17) Most ISE transcriptionally active regions overlap with UCSC Genome Browser ECGene regions Number of human ISE transcriptionally active regions overlapping ECGene predicted exons: 63,361 Median overlap length: 119 nts Median percentage of ECGene exons overlapping ISE transcriptionally active regions: 23% Median percentage of ISE transcriptionally active regions overlapping ECGene exons: 100% Median Hotness for the ISE transcriptionally active regions overlapping ECGene exons: 3.89 Most ISE transcriptionally active regions overlap with UCSC Genome Browser 7X Reg Potential regions Number of human ISE transcriptionally active regions overlapping 7x Reg Potential predicted regions: 60,900 Median overlap length: 108 nts Median percentage of 7x Reg Potential regions overlapping ISE hotspots: 13% Median percentage ISE hotspots overlapping 7x Reg Potential regions: 100% Median Hotness for the ISE hotspots overlapping 7x Reg Potential regions: 3.60

When ESTs align exactly with clean sharp edges, the resulting transcriptionally active regions will have an integer abundance score (FIG. 6). If ESTs are staggered when aligned, the abundance score for the transcriptionally active region will most likely result in a decimal that distances an integer. Given the minimal length cutoff of 20 nucleotides and abundance score cutoff of 3, by our calculation a typical ISE transcriptionally active region has a random chance of less than 0.003 to have integer abundance score.

FIGS. 7A and B show abundance score distributions for the ISE transcriptionally active regions (A) and exons in RefSeq (B). The red ticks mark the location of integers ranging from 3 to 32 in log2 scale and indicate that the spikes are co-located around integers. The abundance score distribution is uneven and is significantly higher (indicated by the spikes in Figure A) around integers. Integer abundance scores are most likely products of clean sharp edged EST alignments, a prominent characteristic of coding RNA exons resulting from precise splicing control. Consistently, this uneven distribution is a characteristic shared with that of known exons in RefSeq (Figure B) transcripts. It indicates that many of these ISE transcriptionally active regions are potentially un-discovered exons. 19,532 ISE transcriptionally active regions were found to have abundance scores equal to or greater than 5.0, and 1462 of those have abundance scores very close to an integer (<=0.01 to the nearest integer). By this estimation, 7.49% of the ISE transcriptionally active regions are likely to be un-discovered exons. This estimation may be largely conservative given that only a small percentage of known RefGene exons have abundance scores close to integers. Since the ISE transcriptionally active regions are mapped into known gene intronic regions, these potentially are un-discovered alternatively spliced exons that may be tissue or cell specific for the known genes.

In summary, intronic sense EST (ISE) transcriptionally active regions represent potentially functional RNA elements in the genome. The functional RNA elements revealed by the ISE transcriptionally active regions may be coding or non-coding regulatory sequences.

EXAMPLE 2

In another example, an expression profile dataset for human tissues was generated from a proprietary sequencing dataset of approximately thirteen million ESTs. Recent evidence indicates that approximately 60% of the mammalian genome is transcribed. The transcribed RNA sequences can be effectively detected through large scale sequencing projects and outputted as expressed sequence tags (ESTs). ESTs mark the transcribed sequences in the genome, frequently map to noncoding regions, i.e., intronic or intergenic gene sequences. These non-coding ESTs may identify functional RNA elements that have significant biological function and biomarker potential. Thus there is a need for an efficient method to identify functional RNA elements through EST sequencing and data analysis.

In this example, an SQL database management system was utilized to create and implement an efficient algorithm that identifies transcriptionally active regions (TARs) in the genome and quantifies their abundances. Using our proprietary LifeSeq EST data, many TARs were identified in the known gene introns in human genome. Although gene introns have been considered as non-functional by conventional wisdom, the TARs discovered by this method along with their significant AbundanceScores indicate they are likely functional. By analysis of large scale tissue specific EST data, TARs were discovered that represent potential biomarkers to differentiate several tumor and normal samples (Tables 2-5).

TABLE 2 Tar_Intestine_Small_Normal_Id GeneIntronId TarStart TarEnd TarAbundanceScore chrom strand GeneId symbol 1 88 8716307 8716571 4.89 chr16 + 18 ABAT 2 88 8730789 8730877 3 chr16 + 18 ABAT 3 231 2322414 2322651 4.89 chr16 − 21 ABCA3 4 247 30647370 30647907 3 chr6 + 23 ABCF1 5 323 130763439 130763676 4 chr9 + 25 ABL1 6 347 133161298 133161407 3 chr9 − 28 ABO 7 375 1006215 1006619 3 chr17 − 29 ABR 8 549 7066909 7066934 3 chr17 + 37 ACADVL 9 549 7066980 7067096 3 chr17 + 37 ACADVL 10 549 7067113 7067174 3.52 chr17 + 37 ACADVL 11 587 28953884 28954085 3 chr17 − 40 ACCN1 12 748 74037606 74037747 3.82 chr2 + 72 ACTG2 13 766 43901084 43901475 3 chr19 + 81 ACTN4 14 793 68418541 68418826 3 chr14 − 87 ACTN1 15 798 68423829 68424157 3.73 chr14 − 87 ACTN1 16 798 68424289 68424327 3 chr14 − 87 ACTN1 17 916 54300973 54301341 3 chr2 + 98 ACYP2 18 963 56770771 56771061 4.86 chr15 − 102 ADAM10 19 977 151384674 151385194 3.89 chr1 − 103 ADAR Normal tissue transcriptionally active regions: the genomic coordinates, abundance scores, and gene information for the transcriptionally active regions (TAR) found in the known gene intronic region sense strand from human normal small intestine tissue. Table only includes the first 20 rows in each sheet for this sample file for the small intestine tissue.

TABLE 3 Tar_Intestine_Small_Tumor_Id GeneIntronId TarStart TarEnd TarAbundanceScore chrom strand GeneId symbol 1 397 32574804 32575372 3 chr17 − 31 ACACA 2 3754 209151625 209151791 3.48 chr1 + 467 ATF3 7 23402 44026130 44026273 3 chr19 − 3191 HNRPL 8 25574 37765267 37765420 3.81 chr3 + 3680 ITGA9 9 29439 150758710 150758762 3.62 chrX + 4103 MAGEA4 11 36092 66697468 66697536 3 chr13 − 5101 PCDH9 13 41506 89695255 89695806 3 chr10 + 5728 PTEN 14 44814 149805307 149805363 3 chr5 − 6208 RPS14 17 49786 29597976 29598032 3 chr16 + 6818 SULT1A3 18 49786 29613486 29613855 3 chr16 + 6818 SULT1A3 19 49786 29820280 29820470 3 chr16 + 6818 SULT1A3 29 49786 29994842 29994916 5.52 chr16 + 6818 SULT1A3 30 49786 29995137 29995297 6 chr16 + 6818 SULT1A3 31 49786 30000081 30000132 6 chr16 + 6818 SULT1A3 32 49786 30001306 30001356 5 chr16 + 6818 SULT1A3 33 49786 30001568 30001669 5 chr16 + 6818 SULT1A3 34 49786 30002216 30002389 4.55 chr16 + 6818 SULT1A3 35 49786 30002477 30002546 3.34 chr16 + 6818 SULT1A3 37 56510 31610906 31610959 3 chr6 − 7919 BAT1 Tumor tissue transcriptionally active regions: the genomic coordinates, abundance scores, and gene information for the transcriptionally active regions (TAR) found in the known gene intronic region sense strand from human Tumor small intestine tissue. Table only includes the first 20 rows in each sheet for this sample file for the small intestine tissue.

TABLE 4 Normal_(—) Normal_(—) Normal_(—) Normal_(—) Tumor_(—) Tumor_(—) Tumor_(—) Tumor_(—) overlap- GeneIntronId TarId TarStart TarEnd TarAbundanceScore TarId TarStart TarEnd TarAbundanceScore Length 3858 51 8780574 8781266 11.08 3 8781040 8781245 3 206 12816 139 58233806 58234184 8.92 4 58233926 58233985 3 60 19424 227 144703744 144704531 17.95 5 144703749 144704430 10.16 682 20070 232 12865916 12866686 29.38 6 12865920 12866603 9.21 684 31679 458 233309165 233309488 8.66 10 233309185 233309471 3 287 39664 536 27527145 27528076 34.8 12 27527323 27528067 4.28 745 44833 598 80838027 80838701 42 15 80838070 80838694 6.24 625 46273 619 11959578 11960257 5.67 16 11959587 11959785 3 199 49786 721 29984597 29984749 18.46 20 29984606 29984749 8.82 144 49786 722 29986056 29986189 22.66 21 29986056 29986188 13.73 133 49786 723 29986272 29986483 7.88 22 29986272 29986483 14.43 212 49786 724 29987468 29987522 16.02 23 29987468 29987522 12.31 55 49786 725 29987640 29987800 15.53 24 29987640 29987800 12.55 161 49786 726 29988128 29988211 9.94 25 29988128 29988211 7.69 84 49786 727 29988321 29988495 12.49 26 29988321 29988495 7.21 175 49786 728 29988652 29988851 15.13 27 29988652 29988851 6.09 200 49786 729 29988939 29989233 6.82 28 29988939 29989193 4.87 255 53627 807 148342968 148343285 18.24 36 148342976 148343528 4.81 310 58366 867 59663510 59663888 8.92 39 59663630 59663689 3 60 Overlapping transcriptionally active regions: the genomic coordinates, abundance scores, and gene information for the overlapping transcriptionally active regions (TAR) found in the known gene intronic region sense strand between human Normal and Tumor small intestine tissues. Table only includes the first 20 rows in each sheet for this sample file for the small intestine tissue.

The overlapping transcriptionally active regions (TARs) identified in Table 4 are likely to be constitutively expressed in small intestine tissue and may be used as the references for normalization in the expression profiling analysis.

Table 5 is a summary of all the TARs compiled from the complete datasets for the above study with abundance score greater or equal to 3.

TABLE 5 Comon TARs Number of Number of overlapping TARs TARs in between Normal and Tissue Type in Normal Tissue Tumor Tissue Tumor tissues Breast 1339 1589 488 Large intestine 1348 1462 517 Small intestine 2722 76 150 Prostate 1126 2395 496 Skin 1083 1828 894

A subset of TARs were selected for our colon and breast cancer biomarker screen according to further examination of public literature and proprietary gene data (Table 6).

TABLE 6 GeneIntronId TarStart TarEnd AbundanceScore Length Chromosome Strand Official Gene Symbol GeneID Breast Normal B_N_1 . . . . . . 26.07 248 chr1 − XXXXX 123456 B_N_2 . . . . . . 15.76 631 chr5 − XXXXX 123456 B_N_3 . . . . . . 15.71 563 chr3 − XXXXX 123456 B_N_4 . . . . . . 15.49 827 chr7 + XXXXX 123456 B_N_5 . . . . . . 15.09 127 chr16 + XXXXX 123456 B_N_6 . . . . . . 14.44 459 chr8 − XXXXX 123456 Breast_Tumor B_T_1 . . . . . . 46.22 493 chr3 + XXXXX 123456 B_T_2 . . . . . . 24.00 99 chr16 + XXXXX 123456 B_T_3 . . . . . . 23.86 79 chr16 + XXXXX 123456 B_T_4 . . . . . . 19.61 272 chr3 + XXXXX 123456 B_T_5 . . . . . . 16.32 1027 chr17 + XXXXX 123456 B_T_6 . . . . . . 14.28 224 chr11 + XXXXX 123456 B_T_7 . . . . . . 14.13 311 chr1 + XXXXX 123456 B_T_8 . . . . . . 14.02 1067 chr6 + XXXXX 123456 B_T_9 . . . . . . 13.76 792 chr9 + XXXXX 123456 B_T_10 . . . . . . 13.75 739 chr11 + XXXXX 123456 Colon_Normal C_N_1 . . . . . . 38.72 208 chr1 + XXXXX 123456 C_N_2 . . . . . . 37.26 327 chr16 + XXXXX 123456 C_N_3 . . . . . . 28.74 664 chr16 + XXXXX 123456 C_N_4 . . . . . . 28.69 360 chr2 − XXXXX 123456 C_N_5 . . . . . . 22.95 159 chr16 + XXXXX 123456 C_N_6 . . . . . . 18.17 446 chr6 + XXXXX 123456 C_N_7 . . . . . . 17.20 492 chr12 − XXXXX 123456 C_N_8 . . . . . . 16.83 467 chr19 − XXXXX 123456 C_N_9 . . . . . . 16.63 150 chr14 + XXXXX 123456 Colon_Tumor C_T_1 . . . . . . 43.00 97 chr20 + XXXXX 123456 C_T_2 . . . . . . 24.92 620 chr2 + XXXXX 123456 C_T_3 . . . . . . 20.07 234 chr19 + XXXXX 123456 C_T_4 . . . . . . 19.24 269 chrX − XXXXX 123456 C_T_5 . . . . . . 13.03 511 chr19 + XXXXX 123456 C_T_6 . . . . . . 12.33 249 chr5 + XXXXX 123456 C_T_7 . . . . . . 11.6 279 chr10 − XXXXX 123456 C_T_8 . . . . . . 11.31 251 chr5 − XXXXX 123456 C_T_9 . . . . . . 11.19 881 chr18 + XXXXX 123456 Gene symbols and specific identifiers have been masked out.

The TARs shown in Table 6 represent tissue specific biomarkers (e.g., biomarkers for differentiations between normal and tumor breast tissues, or normal and tumor colon tissues), and can be utilized as diagnostic or prognostic markers of cancer.

The foregoing discussion of the invention has been presented for purposes of illustration and description. The foregoing is not intended to limit the invention to the form or forms disclosed herein. Although the description of the invention has included description of one or more embodiments and certain variations and modifications, other variations and modifications are within the scope of the invention, e.g., as may be within the skill and knowledge of those in the art, after understanding the present disclosure. It is intended to obtain rights which include alternative embodiments to the extent permitted, including alternate, interchangeable and/or equivalent structures, functions, ranges or steps to those claimed, whether or not such alternate, interchangeable and/or equivalent structures, functions, ranges or steps are disclosed herein, and without intending to publicly dedicate any patentable subject matter. 

The invention claimed is:
 1. A computer-implemented method for determining a quantitative measure for an RNA expression profile of a biological sample containing sequences of nucleotides, the method comprising: for each of a plurality of output sequences from the biological sample: receiving, information of an alignment of the respective output sequence onto a reference sequence that has a nucleotide at each of a plurality of nucleotide positions, the alignment aligning the output sequences to specific nucleotide positions of the reference sequence, wherein at least a portion of the output sequences are derived from RNA, and wherein the reference sequence includes at least a portion of a genome; calculating a count histogram for the reference sequence based on the alignment of the plurality of output sequences onto the reference sequence, the count histogram including a respective coverage count for each nucleotide position of the reference sequence, wherein calculating the count histogram includes: for each nucleotide position of the reference sequence: determining, the corresponding coverage count for the respective nucleotide position by determining the number of output sequences that align to the respective nucleotide position of the reference sequence; locating a first contiguous region in the count histogram where the coverage count for each nucleotide position in the first contiguous region is equal to or greater than a threshold; and calculating a first abundance score for the first contiguous region, wherein an abundance score includes an average of the coverage count for each nucleotide position in a contiguous region; wherein the method is performed by a computer.
 2. The computer-implemented method of claim 1, further comprising: identifying the first contiguous region of the plurality of nucleotide positions of the reference sequence as being a transcriptionally active region.
 3. The computer-implemented method of claim 2, further comprising using the identified transcriptionally active region as a biomarker to differentiate between different types of biological samples.
 4. The computer-implemented method of claim 3, wherein the different types of biological samples include tumor and normal samples.
 5. The computer-implemented method of claim 1, wherein the threshold is a fractional number.
 6. The computer-implemented method of claim 1, wherein the first contiguous region of the reference sequence is required to be equal to or greater than a minimum length.
 7. The computer-implemented method of claim 6, wherein the minimum length is 20 nucleotides, and wherein the threshold is greater than or equal to three.
 8. The computer-implemented method of claim 1, wherein the first abundance score is calculated as a sum of all the coverage counts for the first contiguous region divided by the length of the first contiguous region, and wherein the length is the number of nucleotide positions in the first contiguous region.
 9. The computer-implemented method of claim 1, wherein the human genome is used as the reference sequence.
 10. The computer-implemented method of claim 1, wherein the reference sequence is a predetermined target gene sequence for which the RNA expression profile is being determined.
 11. The computer-implemented method of 10, wherein the threshold is zero for the predetermined target gene sequence.
 12. The computer-implemented method of claim 1, wherein the reference sequence is assembled from a consensus of the output sequences.
 13. The computer-implemented method of claim 1, further comprising: determining a second coverage count for each of the nucleotide positions of the reference sequence using second output sequences from a different biological sample; locating a second contiguous region of the plurality of nucleotide positions where the second coverage count for each nucleotide position is equal to or greater than the threshold; calculating a second abundance score for the second contiguous region; comparing the first abundance score to the second abundance score; and determining that one of the biological samples is a disease sample based on the comparison.
 14. The computer-implemented method of claim 13, further comprising: identifying the second contiguous region for comparison to the first contiguous region by determining an overlap between the first contiguous region and the second contiguous region.
 15. The computer-implemented method of claim 14, wherein determining an overlap between the first contiguous region and the second contiguous region includes: representing each of the first contiguous region and the second contiguous region with reference coordinates based on nucleotide positions of an alignment to the reference sequence, wherein the reference coordinates include sequence start and end positions; and comparing the start and end positions of the first contiguous region to the start and end positions of the second contiguous region.
 16. The computer-implemented method of claim 15, wherein the reference coordinates of a contiguous region are implemented with the start position less than or equal to the end position, or with the start position always greater than or equal to the end position.
 17. The computer-implemented method of claim 15, wherein the reference coordinates are comprised of a chromosome location, strand, a start position and an end position according to an alignment onto the reference sequence.
 18. The computer-implemented method of claim 17, wherein the chromosome location of the first contiguous region equals the chromosome location of the second contiguous region, and wherein the strand of the first contiguous region equals the strand of the second contiguous region.
 19. The computer-implemented method of claim 15, wherein a minimum overlap length is required for a determination that the first contiguous region and the second contiguous region overlap.
 20. The computer-implemented method of claim 1, further comprising: determining an overlap between the first contiguous region and another sequence of nucleotides by: representing each of the first contiguous region and the another sequence with reference coordinates based on nucleotide positions of an alignment to the reference sequence, wherein the reference coordinates include sequence start and end positions; and comparing the start and end positions of the first contiguous region to the start and end positions of the another sequence; and if an overlap exists, associating the first contiguous region with the another sequence.
 21. The computer-implemented method of claim 20, wherein the reference coordinates are comprised of a chromosome location, strand, a start position and an end position according to an alignment onto the reference sequence.
 22. The computer-implemented method of claim 20, wherein a minimum overlap length is required for a determination that the first contiguous region and the another sequence overlap.
 23. A system for determining a quantitative measure for an RNA expression profile of a biological sample containing sequences of nucleotides, the system comprising: a database storing, for each of a plurality of output sequences from the biological sample: information of an alignment of the respective output sequence onto a reference sequence that has a nucleotide at each of a plurality of nucleotide positions, the alignment aligning the output sequences to specific nucleotide positions of the reference sequence, wherein at least a portion of the output sequences are derived from RNA, and wherein the reference sequence includes at least a portion of a genome; and one or more processors that are communicably coupled with the database and that are programmed to: calculate a count histogram for the reference sequence based on the alignment of the plurality of output sequences onto the reference sequence, the count histogram including a respective coverage count for each nucleotide position of the reference sequence, wherein calculating the count histogram includes: for each nucleotide position of the reference sequence: determine the corresponding coverage count for the respective nucleotide position by determining the number of output sequences that align to the respective nucleotide position of the reference sequence; locate a first contiguous region in the count histogram where the coverage count for each nucleotide position in the first contiguous region is equal to or greater than a threshold; and calculate a first abundance score for the first contiguous region, wherein an abundance score includes an average of the coverage count for each nucleotide position in a contiguous region.
 24. The system of claim 23, wherein the one or more processors are further programmed to: determine a second coverage count for each of the nucleotide positions of the reference sequence using second output sequences from a different biological sample; locate a second contiguous region of the plurality of nucleotide positions where the second coverage count for each nucleotide position is equal to or greater than the threshold; calculate a second abundance score for the second contiguous region; compare the first abundance score to the second abundance score; and determine that one of the biological samples is a disease sample based on the comparison.
 25. The system of claim 23, further comprising: a computer output device that is coupled with the one or more processors and that displays the first contiguous region with the first abundance score.
 26. A computer-implemented method for determining a transcriptionally active region of a reference sequence associated with a biological sample containing sequences of nucleotides, the method comprising: for each of a plurality of output sequences from the biological sample: receiving, information of an alignment of the respective output sequence onto a reference sequence that has a nucleotide at each of a plurality of nucleotide positions, the alignment aligning the output sequences to specific nucleotide positions of the reference sequence, wherein at least a portion of the output sequences are derived from RNA, and wherein the reference sequence includes at least a portion of a genome; calculating a count histogram for the reference sequence based on the alignment of the plurality of output sequences onto the reference sequence, the count histogram including a respective coverage count for each nucleotide position of the reference sequence, wherein calculating the count histogram includes: for each nucleotide position of the reference sequence: determining, the corresponding coverage count for the respective nucleotide position by determining the number of output sequences that align to the respective nucleotide position of the reference sequence; locating a first contiguous region in the count histogram where the coverage count for each nucleotide position in the first contiguous region is equal to or greater than a threshold; and identifying the first contiguous region of the plurality of nucleotide positions of the reference sequence as being a transcriptionally active region; wherein the method is performed by a computer.
 27. The computer-implemented method of claim 26, further comprising using the identified transcriptionally active region as a biomarker to differentiate between different types of biological samples.
 28. The computer-implemented method of claim 27, wherein the different types of biological samples include tumor and normal samples.
 29. The computer-implemented method of claim 26, wherein the first contiguous region of the reference sequence is required to be equal to or greater than a minimum length.
 30. The computer-implemented method of claim 29, wherein the minimum length is 20 nucleotides, and wherein the threshold is greater than or equal to three.
 31. The computer-implemented method of claim 26, wherein the human genome is used as the reference sequence.
 32. The computer-implemented method of claim 26, wherein the reference sequence is a predetermined target gene sequence for which the RNA expression profile is being determined.
 33. The computer-implemented method of claim 32, wherein the threshold is zero for the predetermined target gene sequence.
 34. The computer-implemented method of claim 26, wherein the reference sequence is assembled from a consensus of the output sequences.
 35. The computer-implemented method of claim 26, further comprising: determining an overlap between the first contiguous region and another sequence of nucleotides by: representing each of the first contiguous region and the another sequence with reference coordinates based on nucleotide positions of an alignment to the reference sequence, wherein the reference coordinates include sequence start and end positions; and comparing the start and end positions of the first contiguous region to the start and end positions of the another sequence; and if an overlap exists, associating the first contiguous region with the another sequence.
 36. The computer-implemented method of claim 35, wherein the reference coordinates are comprised of a chromosome location, strand, a start position and an end position according to an alignment onto the reference sequence.
 37. The computer-implemented method of claim 35, wherein a minimum overlap length is required for a determination that the first contiguous region and the another sequence overlap.
 38. A system for determining a transcriptionally active region of a reference sequence associated with a biological sample containing sequences of nucleotides, the system comprising: a database storing, for each of a plurality of output sequences from the biological sample: information of an alignment of the respective output sequence onto a reference sequence that has a nucleotide at each of a plurality of nucleotide positions, the alignment aligning the output sequences to specific nucleotide positions of the reference sequence, wherein at least a portion of the output sequences are derived from RNA, and wherein the reference sequence includes at least a portion of a genome; and one or more processors that are communicably coupled with the database and that are programmed to: calculate a count histogram for the reference sequence based on the alignment of the plurality of output sequences onto the reference sequence, the count histogram including a respective coverage count for each nucleotide position of the reference sequence, wherein calculating the count histogram includes: for each nucleotide position of the reference sequence: determine the corresponding coverage count for the respective nucleotide position by determining the number of output sequences that align to the respective nucleotide position of the reference sequence; locate a first contiguous region in the count histogram where the coverage count for each nucleotide position in the first contiguous region is equal to or greater than a threshold; and identify the first contiguous region of the plurality of nucleotide positions of the reference sequence as being a transcriptionally active region.
 39. The system of claim 38, further comprising: a computer output device that is coupled with the one or more processors and that displays the first contiguous region as being a transcriptionally active region. 